A Combined STD-NMR/Molecular Modeling Protocol for Predicting the Binding Modes of the Glycosidase Inhibitors Kifunensine and Salacinol to Golgi α-Mannosidase II
Bibliographic record
Abstract
A combined STD-NMR/molecular modeling protocol to probe the binding modes of the glycosidase inhibitors kifunensine and salacinol to Drosophila melanogaster Golgi alpha-mannosidase II (dGMII) was tested. Saturation-transfer difference (STD) NMR experiments were carried out for the complexes of dGMII with these two inhibitors. The program AutoDock 3.0 was then used to optimize the interactions of the inhibitors with the residues in the active site of dGMII. Theoretical STD effects of the ligand protons in the complexes were calculated for the different binding modes with the recently developed CORCEMA-ST protocol. Comparison of experimental and theoretical effects then permitted selection of the likely binding modes of the ligands. The more rigid kifunensine was used initially to test the protocol. Excellent correlation between experimental and theoretical data was obtained for one of the binding modes that also corresponded to that observed in the crystal structure of the complex. The protocol was then extended to the more flexible salacinol. For the selected binding mode, good correlation of experimental and theoretical data for the five-membered ring was obtained; however, poor correlation for protons on the acyclic chain was obtained, suggesting flexibility in this portion of the molecule. Comparison of the selected binding mode with that from a crystal structure of salacinol with dGMII showed excellent superimposition of the five-membered ring but another orientation of the acyclic chain. The results suggest that reliable structural binding modes of a ligand to protein in aqueous solution can be provided with the combined use of STD-NMR spectroscopy, molecular modeling, and CORCEMA-ST calculations, although highly flexible portions of the ligand may be poorly defined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".